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662 lines
26 KiB
Python
662 lines
26 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py
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"""Inference-only Deepseek-VL2 model compatible with HuggingFace weights."""
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Annotated, Literal, Optional, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from transformers import BatchFeature
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from vllm.config import VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor import SamplingMetadata
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.model_loader.utils import set_default_torch_dtype
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from vllm.model_executor.models.transformers import replace_linear_class
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
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MultiModalKwargsItems, MultiModalUUIDDict,
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NestedTensors)
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from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
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ImageSize, MultiModalDataItems)
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo,
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MultiModalProcessingInfo,
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PromptReplacement, PromptUpdate)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
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MlpProjectorConfig,
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VisionEncoderConfig)
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from vllm.transformers_utils.processors.deepseek_vl2 import (
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DeepseekVLV2Processor)
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from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
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from vllm.utils import is_list_of
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
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from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
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init_vllm_registered_model, maybe_prefix,
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merge_multimodal_embeddings)
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# The image token id may be various
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_IMAGE_TOKEN = "<image>"
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class DeepseekVL2ImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- p: Number of patches
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- c: Number of channels (3)
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- h: Height of each image
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- w: Width of each image
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"""
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type: Literal["pixel_values"]
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data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "p", 3, "h", "w", dynamic_dims={"p"})]
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images_spatial_crop: Annotated[torch.Tensor, TensorShape("bn", 2)]
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class DeepseekVL2VImageEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- f: Image feature size
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- h: Hidden size (must match language model backbone)
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"""
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type: Literal["image_embeds"]
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data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "f", "h")]
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DeepseekVL2ImageInputs = Union[DeepseekVL2ImagePixelInputs,
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DeepseekVL2VImageEmbeddingInputs]
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class MlpProjector(nn.Module):
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def __init__(self, cfg: MlpProjectorConfig):
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super().__init__()
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self.cfg = cfg
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assert not cfg.token_pooling, (
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"Token pooling is not supported currently.")
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if cfg.projector_type == "downsample_mlp_gelu":
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mlp_depth = cfg.depth
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mlp_ratio = cfg.mlp_ratio
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modules = [
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nn.Linear(
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cfg.input_dim * cfg.downsample_ratio *
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cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
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]
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for _ in range(1, mlp_depth - 1):
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modules.append(nn.GELU())
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modules.append(
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nn.Linear(cfg.n_embed * mlp_ratio,
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cfg.n_embed * mlp_ratio))
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
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modules = nn.Sequential(*modules)
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else:
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raise NotImplementedError(
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f"Unsupported projector type: {cfg.projector_type}")
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self.layers = modules
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def forward(self, x):
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bs, hw, input_dim = x.shape
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h = w = int((hw)**0.5)
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"""compute padding"""
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if h % self.cfg.downsample_ratio:
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pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
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else:
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pad = 0
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x = x.reshape(bs, h, w, input_dim)
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if pad > 0:
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x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
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"""4 to 1 concat"""
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x = x.permute(0, 3, 1, 2) # B, C, H, W
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x = F.unfold(x,
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kernel_size=self.cfg.downsample_ratio,
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stride=self.cfg.downsample_ratio,
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padding=0) # B, C*4, HW // 4
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x = x.permute(0, 2, 1)
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return self.layers(x)
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class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(DeepseekVLV2Config)
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def get_hf_processor(self, **kwargs: object):
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return self.ctx.get_hf_processor(DeepseekVLV2Processor, **kwargs)
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": None}
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def get_num_image_tokens(self,
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*,
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image_width: int,
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image_height: int,
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cropping: bool = True) -> int:
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hf_processor = self.get_hf_processor()
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image_size = hf_processor.image_size
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patch_size = hf_processor.patch_size
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downsample_ratio = hf_processor.downsample_ratio
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if cropping:
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best_width, best_height = hf_processor.select_best_resolution(
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(image_width, image_height))
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num_width_tiles, num_height_tiles = (best_width // image_size,
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best_height // image_size)
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else:
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num_width_tiles = num_height_tiles = 1
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h = w = math.ceil((image_size // patch_size) / downsample_ratio)
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global_views_tokens = h * (w + 1)
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local_views_tokens = (num_height_tiles * h) * (num_width_tiles * w + 1)
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return global_views_tokens + local_views_tokens + 1
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def get_image_size_with_most_features(self) -> ImageSize:
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hf_config = self.get_hf_config()
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candidate_resolutions = hf_config.candidate_resolutions
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height, width = max(candidate_resolutions,
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key=lambda x: self.get_num_image_tokens(
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image_width=x[1], image_height=x[0]))
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return ImageSize(width=width, height=height)
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class DeepseekVL2DummyInputsBuilder(
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BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_images = mm_counts.get("image", 0)
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processor = self.info.get_hf_processor()
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image_token = processor.image_token
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return image_token * num_images
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> MultiModalDataDict:
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num_images = mm_counts.get("image", 0)
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max_image_size = self.info.get_image_size_with_most_features()
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return {
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"image":
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self._get_dummy_images(width=max_image_size.width,
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height=max_image_size.height,
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num_images=num_images)
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}
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class DeepseekVL2MultiModalProcessor(
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BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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tok_kwargs: Mapping[str, object],
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) -> BatchFeature:
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if not mm_data:
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tokenizer = self.info.get_tokenizer()
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return tokenizer(prompt,
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add_special_tokens=True,
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return_tensors="pt")
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processed_outputs = super()._call_hf_processor(
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prompt=prompt,
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mm_data=mm_data,
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mm_kwargs=mm_kwargs,
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tok_kwargs=tok_kwargs,
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)
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pixel_values = processed_outputs["pixel_values"]
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# split pixel values into patches corresponding to each image
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images_spatial_crop = processed_outputs["images_spatial_crop"]
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patches_per_image = [x.prod().item() + 1 for x in images_spatial_crop]
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pixel_values = pixel_values.split(patches_per_image)
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processed_outputs["pixel_values"] = pixel_values
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return processed_outputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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images_spatial_crop=MultiModalFieldConfig.batched("image"),
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image_embeds=MultiModalFieldConfig.batched("image"),
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)
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def _get_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargsItems,
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) -> Sequence[PromptUpdate]:
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hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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image_token_id = hf_processor.image_token_id
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assert isinstance(image_token_id, int)
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def get_replacement_deepseek_vl2(item_idx: int):
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images = mm_items.get_items(
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"image", (ImageEmbeddingItems, ImageProcessorItems))
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if isinstance(images, ImageEmbeddingItems):
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num_image_tokens = images.get_feature_size(item_idx)
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else:
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image_size = images.get_image_size(item_idx)
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num_image_tokens = self.info.get_num_image_tokens(
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image_width=image_size.width,
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image_height=image_size.height,
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cropping=len(images) <= 2,
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)
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return [image_token_id] * num_image_tokens
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return [
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PromptReplacement(
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modality="image",
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target=[image_token_id],
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replacement=get_replacement_deepseek_vl2,
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)
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]
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def _cached_apply_hf_processor(
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self,
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prompt: Union[str, list[int]],
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mm_data_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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tokenization_kwargs: Mapping[str, object],
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mm_uuids: Optional[MultiModalUUIDDict] = None,
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) -> tuple[list[int], MultiModalProcessingInfo, bool]:
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# The processor logic is different for len(images) <= 2 vs > 2
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# Since the processing cache assumes that the processor output is
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# invariant of how many images are passed per prompt, we only
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# perform caching for the most common case
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if mm_data_items.get_count("image", strict=False) > 2:
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return self._apply_hf_processor(
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prompt=prompt,
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mm_data_items=mm_data_items,
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hf_processor_mm_kwargs=hf_processor_mm_kwargs,
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tokenization_kwargs=tokenization_kwargs,
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mm_uuids=mm_uuids,
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)
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return super()._cached_apply_hf_processor(
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prompt=prompt,
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mm_data_items=mm_data_items,
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hf_processor_mm_kwargs=hf_processor_mm_kwargs,
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tokenization_kwargs=tokenization_kwargs,
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mm_uuids=mm_uuids,
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)
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@MULTIMODAL_REGISTRY.register_processor(
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DeepseekVL2MultiModalProcessor,
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info=DeepseekVL2ProcessingInfo,
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dummy_inputs=DeepseekVL2DummyInputsBuilder)
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class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
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hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
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"language.": "language_model.",
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})
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@classmethod
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def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
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if modality.startswith("image"):
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return "<image>"
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raise ValueError("Only image modality is supported")
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config: DeepseekVLV2Config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.multimodal_config = multimodal_config
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self.vision_config = config.vision_config
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self.projector_config = config.projector_config
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self.text_config = config.text_config
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model_config = vllm_config.model_config
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tokenizer = cached_tokenizer_from_config(model_config)
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self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]
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self.vision = self._init_vision_module(self.vision_config,
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quant_config,
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maybe_prefix(prefix, "vision"))
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self.projector = MlpProjector(self.projector_config)
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self.tile_tag = config.tile_tag
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self.global_view_pos = config.global_view_pos
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# special token for image token sequence format
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embed_std = 1 / torch.sqrt(
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torch.tensor(self.projector_config.n_embed, dtype=torch.float32))
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if self.tile_tag == "2D":
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# <|view_seperator|>, <|\n|>
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self.image_newline = nn.Parameter(
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torch.randn(self.projector_config.n_embed) * embed_std)
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# This is a typo in original implementation
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self.view_seperator = nn.Parameter(
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torch.randn(self.projector_config.n_embed) * embed_std)
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else:
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raise ValueError(
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f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
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)
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if self.text_config.topk_method == "noaux_tc":
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architectures = ["DeepseekV3ForCausalLM"]
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elif not self.text_config.use_mla:
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architectures = ["DeepseekForCausalLM"]
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else:
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architectures = ["DeepseekV2ForCausalLM"]
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=self.text_config,
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prefix=maybe_prefix(prefix, "language"),
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architectures=architectures,
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)
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors)
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def _get_parent_and_attr(self, root: torch.nn.Module, dotted_name: str):
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"""Return (parent_module, final_attr_name) for a dotted module path."""
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names = dotted_name.split('.')
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parent = root
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for n in names[:-1]:
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parent = getattr(parent, n)
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return parent, names[-1]
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#patch for timm ViT instance to support tensor parallel
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def patch_vit_for_tp(self, vit: torch.nn.Module,
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quant_config: QuantizationConfig):
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try:
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import timm
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except ImportError as e:
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raise ImportError("Please install timm") from e
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for name, module in vit.named_modules():
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if isinstance(module, nn.Linear):
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parent, attr_name = self._get_parent_and_attr(vit, name)
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if isinstance(parent, timm.layers.Mlp) and attr_name == "fc1":
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new_linear = replace_linear_class(module,
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"colwise",
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quant_config,
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prefix=name)
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setattr(parent, attr_name, new_linear)
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elif isinstance(parent,
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timm.layers.Mlp) and attr_name == "fc2":
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new_linear = replace_linear_class(module,
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"rowwise",
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quant_config,
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prefix=name)
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setattr(parent, attr_name, new_linear)
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return vit
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def _init_vision_module(
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self,
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vision_config: VisionEncoderConfig,
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quant_config: Optional[QuantizationConfig],
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prefix: str = "",
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) -> nn.Module:
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# TODO: refactor vision model through timm wrapper from transformers
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try:
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import timm
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except ImportError as e:
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raise ImportError("Please install timm") from e
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with set_default_torch_dtype(torch.float16):
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model = timm.create_model(
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"vit_so400m_patch14_siglip_384.webli",
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pretrained=False,
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num_classes=0,
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dynamic_img_size=True,
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dynamic_img_pad=True,
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)
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if get_tensor_model_parallel_world_size() > 1:
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model = self.patch_vit_for_tp(model, quant_config)
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model = model.to(dtype=torch.get_default_dtype())
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return model
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def _parse_and_validate_image_input(
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self, **kwargs: object) -> Optional[DeepseekVL2ImageInputs]:
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pixel_values = kwargs.pop("pixel_values", None)
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images_spatial_crop = kwargs.pop("images_spatial_crop", None)
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values is None and image_embeds is None:
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return None
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if pixel_values is not None:
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expected_h = expected_w = self.vision_config.image_size
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return DeepseekVL2ImagePixelInputs(type="pixel_values",
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data=flatten_bn(pixel_values),
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images_spatial_crop=flatten_bn(
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images_spatial_crop,
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concat=True),
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resolve_bindings={
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"h": expected_h,
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"w": expected_w,
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})
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if image_embeds is not None:
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return DeepseekVL2VImageEmbeddingInputs(
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type="image_embeds",
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data=flatten_bn(image_embeds),
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)
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raise AssertionError("This line should be unreachable.")
|
|
|
|
def _pixel_values_to_embedding(
|
|
self,
|
|
pixel_values: NestedTensors,
|
|
images_spatial_crop: torch.Tensor,
|
|
) -> NestedTensors:
|
|
# Pixel_values: n_image * batch_size * [patch_per_img, 3, height, width]
|
|
total_tiles = [x for x in pixel_values]
|
|
|
|
# [batch_all_tiles, 3, height, width]
|
|
total_tiles = torch.cat(total_tiles, dim=0)
|
|
|
|
# [batch_all_tiles, vit_seq_len, c]
|
|
images_feature = self.vision.forward_features(total_tiles)
|
|
|
|
# [batch_all_tiles, hw, D]
|
|
images_embeds = self.projector(images_feature)
|
|
|
|
_, hw, n_dim = images_embeds.shape
|
|
h = w = int(hw**0.5)
|
|
|
|
# fill image token based on self.tile_tag & self.global_view_pos
|
|
tile_index = 0
|
|
vision_embeddings = []
|
|
for jdx in range(images_spatial_crop.size(0)):
|
|
# extra global & local features
|
|
num_width_tiles, num_height_tiles = images_spatial_crop[jdx]
|
|
if num_width_tiles == 0 or num_height_tiles == 0:
|
|
break
|
|
num_tiles_in_image = num_width_tiles * num_height_tiles
|
|
|
|
# [hw, D]
|
|
global_features = images_embeds[tile_index]
|
|
|
|
# [num_height_tiles * num_width_tiles, hw, D]
|
|
local_features = images_embeds[tile_index + 1:tile_index + 1 +
|
|
num_tiles_in_image]
|
|
tile_index += num_tiles_in_image + 1
|
|
|
|
# format global and local features
|
|
# ----------------- global view add newline -----------------
|
|
# [hw, D] -> [h, w, D]
|
|
global_features = global_features.view(h, w, n_dim)
|
|
|
|
# [D] -> [h, 1, D]
|
|
new_lines_in_global = repeat(self.image_newline, "d -> h 1 d", h=h)
|
|
|
|
# cat([h, w, D], [h, 1, D], dim=1) -> [h, w + 1, D]
|
|
global_features = torch.cat([global_features, new_lines_in_global],
|
|
dim=1)
|
|
|
|
# [h, w + 1, D] -> [h * (w + 1), D]
|
|
global_features = global_features.view(-1, n_dim)
|
|
|
|
# ----------------- local view add newline -----------------
|
|
# [num_height_tiles * num_width_tiles, h * w, D] ->
|
|
# [num_height_tiles * h, num_width_tiles * w, D]
|
|
local_features = rearrange(local_features,
|
|
"(th tw) (h w) d -> (th h) (tw w) d",
|
|
th=num_height_tiles,
|
|
tw=num_width_tiles,
|
|
h=h,
|
|
w=w)
|
|
|
|
# [D] -> [num_height_tiles * h, 1, D]
|
|
new_lines_in_local = repeat(self.image_newline,
|
|
"d -> (th h) 1 d",
|
|
th=num_height_tiles,
|
|
h=h)
|
|
|
|
# [num_height_tiles * h, num_width_tiles * w + 1, D]
|
|
local_features = torch.cat([local_features, new_lines_in_local],
|
|
dim=1)
|
|
|
|
# [num_height_tiles * h, num_width_tiles * w + 1, D]
|
|
# --> [(num_height_tiles * h) * (num_width_tiles * w + 1), D]
|
|
local_features = local_features.view(-1, n_dim)
|
|
|
|
# merge global and local tiles
|
|
if self.global_view_pos == "head":
|
|
global_local_features = torch.cat([
|
|
global_features,
|
|
self.view_seperator[None, :],
|
|
local_features,
|
|
])
|
|
else:
|
|
global_local_features = torch.cat([
|
|
local_features,
|
|
self.view_seperator[None, :],
|
|
global_features,
|
|
])
|
|
|
|
vision_embeddings.append(global_local_features)
|
|
return vision_embeddings
|
|
|
|
def _process_image_input(
|
|
self, image_input: DeepseekVL2ImageInputs) -> torch.Tensor:
|
|
if image_input["type"] == "image_embeds":
|
|
image_data = image_input["data"]
|
|
if is_list_of(image_data, torch.Tensor):
|
|
# it's already a list of tensors
|
|
return image_data
|
|
if len(image_data.shape) == 3:
|
|
# 3D tensor
|
|
return list(torch.unbind(image_data, dim=0))
|
|
raise ValueError(
|
|
"We expect batched 2D tensors; "
|
|
"this can be either a list of 2D tensors or a single 3D tensor."
|
|
)
|
|
|
|
pixel_values = image_input["data"]
|
|
images_spatial_crop = image_input["images_spatial_crop"]
|
|
|
|
return self._pixel_values_to_embedding(
|
|
pixel_values=pixel_values, images_spatial_crop=images_spatial_crop)
|
|
|
|
def get_language_model(self) -> torch.nn.Module:
|
|
return self.language_model
|
|
|
|
def get_multimodal_embeddings(self,
|
|
**kwargs: object) -> MultiModalEmbeddings:
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
if image_input is None:
|
|
return []
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
return vision_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
if multimodal_embeddings is not None \
|
|
and len(multimodal_embeddings) != 0:
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, multimodal_embeddings,
|
|
self.image_token_id)
|
|
return inputs_embeds
|
|
|
|
def forward(self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object):
|
|
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
|
# condition is for v0 compatibility
|
|
elif inputs_embeds is None:
|
|
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
vision_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model(input_ids,
|
|
positions,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
|
|
loader = AutoWeightsLoader(self)
|
|
autoloaded_weights = loader.load_weights(weights,
|
|
mapper=self.hf_to_vllm_mapper)
|
|
return autoloaded_weights
|